Previous measures focus on computing a measure of dependency. Yet, many authors used different ways to compute covariance-like operators, directly as in [1]}, or based on information theory [2]}, or using more advanced notions of covariance based on embedding, possibly with kernels. We refer for instance to [3]} for a review. Each method chooses a measure of dependency and computes a fairness measure of either the outcome of the algorithmic model or its residuals (or any appropriate transformation) with the sensitive parameter.
